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[pt] Levando em consideração as modificações causadas pelas tecnologias digitais nos processos de ensino-aprendizagem, esta dissertação tem como objetivo investigar se há relação do transtorno da nomofobia com as estratégias de aprendizagem e a crença de autoeficácia no processo de aprendizagem de estudantes universitários. Como principais referenciais teóricos, temos: King, Nardi, Cardoso, Yildirim e Correia para fundamentar a teoria da Nomofobia. Para falar de estratégias de aprendizagem e crenças de autoeficácia, partimos de uma perspectiva sociocognitiva, mais especificamente, da Teoria Social Cognitiva de Albert Bandura e de autores que a usam como referência como base teórica (Zimmerman, Polydoro e Boruchovitch). Esta investigação utilizou método misto e a produção dos dados foi feita em dois estudos. No primeiro estudo enviamos um formulário on-line com uma ficha de caracterização, a Escala de Autoeficácia na Formação Superior, o fator 1 da Escala de Estratégias de Aprendizagem para estudantes universitários e o questionário Nomophobia Questionnaire em sua versão adaptada. Participaram do primeiro estudo 257 universitários. Para o segundo estudo, foram realizadas entrevistas semiestruturadas individuais com cinco estudantes selecionados da base da primeira fase. Os resultados apontam para uma correlação positiva entre a incapacidade de comunicação e a autoeficácia em ações e na formação superior e o escore total da Escala de Autoeficácia na Formação Superior (AEFS). Houve também correlação negativa entre todos os fatores de autoeficácia e o escore total do questionário de nomofobia com a dimensão de estratégias de autorregulação cognitivas e metacognitivas da Escala de Estratégias de Aprendizagem. Os dados produzidos podem contribuir para a compreensão da relação das tecnologias digitais com o processo de aprendizagem. Do mesmo modo, auxiliam no entendimento sobre a percepção que os estudantes têm sobre a sua capacidade de executar tarefas acadêmicas fazendo uso dessas tecnologias e como as empregam nas estratégias cognitivas e metacognitivas. Consideramos que, de modo geral, esta pesquisa pode contribuir para a conscientização dos efeitos negativos do uso dependente do celular e contribuir para que os estudantes desenvolvam estratégias de aprendizagem autorreguladas. Nesse sentido, acreditamos que o desenvolvimento da autorregulação e o uso das estratégias de aprendizagem autorreguladas, bem como, o incentivo do uso consciente das tecnologias digitais, podem contribuir para a prevenção do efeito deletério dos comportamentos nomofóbicos no processo de aprendizagem. / [en] Taking into account the changes caused by digital technologies in the teaching-learning processes, this dissertation aims to investigate whether there is a relationship between nomophobia disorder and learning strategies and the belief in self-efficacy in the learning process of university students. As main theoretical references, we have: King, Nardi, Cardoso, Yildirim and Correia to support the theory of Nomophobia. To talk about learning strategies and self-efficacy beliefs, we start from a socio-cognitive perspective, more specifically, Albert Bandura s Social Cognitive Theory and authors who use it as a reference as a theoretical basis (Zimmerman, Polydoro and Boruchovitch). This investigation used a mixed method and data production was carried out in two stages. In the first stage, we launched an online form with a characterization sheet, the Self-Efficacy Scale in Higher Education, factor 1 of the Learning Strategies Scale for university students and the Nomophobia Questionnaire in its adapted version. 257 university students participated in the first stage. For the second stage, individual semi-structured interviews were carried out with five students selected from the base of the first stage. The results point to a positive correlation between the inability to communicate and self-efficacy in actions and in higher education and the total score of the Self-Efficacy Scale in Higher Education. There was also a negative correlation between all self-efficacy factors and the total score on the nomophobia questionnaire with the cognitive and metacognitive self-regulation strategies dimension of the Learning Strategies Scale. The data produced can contribute to understanding the relationship between digital technologies and the learning process. Likewise, they help to understand the perception that students have about their ability to perform academic tasks using these technologies and how they use them in cognitive and metacognitive strategies. We believe that, in general, this research can contribute to the awareness of the negative effects of dependent cell phone use and help students to develop self-regulated learning strategies. In this sense, we believe that the development of self-regulation and the use of self-regulated learning strategies, as well as encouraging the conscious use of digital technologies, can contribute to preventing the deleterious effect of nomophobic behaviors on the learning process.

<p>  </p> <p>First-Year Engineering (FYE) programs are a critical part of engineering education, yet they are quite complex settings. Given the importance and complexity of FYE programs, research to better understand student learning and inform design and assessment in FYE programs is imperative. Therefore, this dissertation showcases various uses of data analytics and educational theory to support decision-making when designing and assessing FYE programs. Three case studies shape this dissertation work. Each study encompasses a variety of educational data sources, analytical methods, and decision-making tools to produce valuable findings for FYE classrooms. In addition, this dissertation also discusses the potential for incorporating data analytics into FYE programs. A more detailed description of the research methods, a summary of findings, and a list of resulting publications for each case study follows.</p> <p>The first case study investigated the relationship between two related Computational Thinking (CT) practices, data practices and computational problem-solving practices, in acquiring other CT competencies in a large FYE course setting. This study explored the following research questions: (1) What are the different student profiles that characterize their foundational CT practices at the beginning of the semester? and (2) Within these profiles, what are the progressions that students follow in the acquisition of advanced CT practices? To answer these questions, N-TARP Clustering, a novel machine learning algorithm, and sound statistical tools were used to analyze assessment data from the course at the learning objective level. Such a hybrid approach was needed due to the high-dimensionality and homogeneity characteristics of the assessment. It was found that early mastery of troubleshooting and debugging is linked to the successful acquisition of more complex CT competencies. This research was published in an article in the journal <em>IEEE Access</em>.</p> <p>The second case study examined self-regulation components associated with students' successful acquisition of CT skills using students' reflections and assessment data. This research was grounded in three subprocesses of the Self-Regulated Learning (SRL) theory: strategic planning, access to feedback, and self-evaluation. This study responded to the following research question: What is the relationship between SRL subprocesses: access to feedback, self-evaluation, strategic planning, and the acquisition of CT skills in an FYE course? Results from a structural equation model, which reflects the complexity and multidimensionality of the analysis, provided evidence of the relevance of the three subprocesses in the acquisition of CT skills and highlighted the importance of self-assessment as key to success in the acquisition of programming skills. Furthermore, self-assessment was found to effectively represent the task strategy and access to feedback from the students. This analysis led to the understanding that even though the three SRL subprocesses are relevant for the student's success, self-evaluation serves as a catalyst between strategic planning and access to feedback. A resulting article from this case study will be submitted to the <em>International Journal of Engineering Education</em> in the future.</p> <p>Lastly, the third study aimed to predict the students' learning outcomes using data from the Learning Management System (LMS) in an FYE course. The following research questions were explored in this case study: (1) What type of LMS objects contain information to explain students' grades in a FYE course? (2) Is the inclusion of a human operator during the data transformation process significant to the analysis of learning outcomes? Two different sections of a large FYE course were used, one serving as a training data set and the other one as a testing data set. Two logistic regression models were trained. The first model corresponded to a common approach for building a predictive model, using the data from the LMS directly. The second model considered the specifics of the course by transforming the data from aggregate user interaction to more granular categories related to the content of the class. A comparison was made between the predictive measures, e.g., precision, accuracy, recall, and F1 score for both models. The findings from the transformed data set indicate that students' engagement with the career exploration curriculum was the strongest predictor of students' final grades in the course. This is a fascinating finding because the amount of weight the career assignments contributed to the overall course grade was relatively low. This study will be presented at the 2022 American Society of Engineering Education (ASEE) national conference in Minneapolis, Minnesota.</p>

Indiana University-Purdue University Indianapolis (IUPUI) / Objective: To explore and compare the use of metacognitive, cognitive, and environmental resource management self regulatory learning (SRL) strategies used by a national sample of students enrolled in traditional and accelerated baccalaureate nursing programs. Background: Learner focused reforms in nursing education require students to assume more responsibility for learning. Nursing student responsibility for learning is reflected in their use of metacognitive, cognitive, and environmental resource SRL strategies. Learning strategy use promotes the development of clinical reasoning and lifelong learning skills requisite to meet the needs of complex patients in a dynamic healthcare environment. Method: Using Bandura’s social cognitive theory as a framework, the learning subscales of the Motivated Strategies for Learning Questionnaire were used to survey a national sample of 514 baccalaureate nursing students enrolled in their final semester of a traditional baccalaureate nursing program or a 12-month accelerated baccalaureate program. Delineation of student use of metacognitive, cognitive (rehearsal, organization, and elaboration), and environmental resource management (help seeking, peer learning, effort regulation, and time and study environment) SRL strategies was examined by program and in light of age, grade point average (GPA), weekly hours spent studying independently, and weekly hours spent in employment. Results: Differences in SRL strategy use were found between the program groups and between program groups divided by sample age. Older students in both the accelerated and traditional programs used more metacognition and elaboration SRL strategies than their younger traditional counterparts. Older traditional students used significantly more effort regulation SRL strategies than both groups of younger students. Both older groups of students studied significantly more, used significantly more time and study environment SRL strategies, and had significantly higher GPAs than the younger groups of students from both programs. Conclusions: This study provides a framework for learner focused nursing education by explicitly defining differences in SRL strategy use of students enrolled in traditional and accelerated baccalaureate nursing programs.

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